Optimization configuration method for new energy and energy storage in incremental distribution network based on adaptive plant cell swarm algorithm

The increasing integration of new energy sources and energy storage systems into incremental distribution networks (IDNs) has posed significant challenges for optimal configuration, balancing economic efficiency, system reliability, and renewable energy utilization. This paper proposes an optimization configuration method based on the Adaptive Plant Cell Swarm Algorithm (APCSA). Inspired by the growth and division mechanisms of plant cells, APCSA incorporates adaptive strategies to enhance its global search and local exploitation capabilities, addressing the dynamic and complex nature of IDNs. The optimization model considers multiple objectives, including cost minimization, voltage stability, and energy storage efficiency, while adhering to power balance and network capacity constraints. A comprehensive case study demonstrates the superior performance of APCSA in identifying optimal configurations compared to conventional algorithms. Results highlight its robustness, computational efficiency, and ability to adapt to varying network conditions. This study provides a practical and scalable solution for enhancing the economic and operational performance of IDNs with integrated renewable energy and storage systems.